Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension...

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Vydáno v:	IEEE internet of things journal Ročník 6; číslo 4; s. 6822 - 6834
Hlavní autoři:	Zolanvari, Maede, Teixeira, Marcio A., Gupta, Lav, Khan, Khaled M., Jain, Raj
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 01.08.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Analytics Anomalies Artificial intelligence Cyber attack Cybersecurity Electromagnetic interference Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Internet of Things intrusion detection Intrusion detection systems Literature reviews Machine learning machine learning (ML) network security Protocol (computers) Query languages Structured Query Language-SQL supervisory control and data acquisition (SCADA) Vulnerability vulnerability assessment
ISSN:	2327-4662, 2327-4662
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Abstract	It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
AbstractList	It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.
Author	Zolanvari, Maede Khan, Khaled M. Jain, Raj Teixeira, Marcio A. Gupta, Lav
Author_xml	– sequence: 1 givenname: Maede orcidid: 0000-0003-1428-7770 surname: Zolanvari fullname: Zolanvari, Maede email: maede.zolanvari@wustl.edu organization: Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA – sequence: 2 givenname: Marcio A. orcidid: 0000-0003-1466-3596 surname: Teixeira fullname: Teixeira, Marcio A. organization: Federal Institute of Education, Science and Technology of São Paulo, São Paulo, Brazil – sequence: 3 givenname: Lav orcidid: 0000-0002-0524-654X surname: Gupta fullname: Gupta, Lav organization: Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA – sequence: 4 givenname: Khaled M. orcidid: 0000-0002-8848-0760 surname: Khan fullname: Khan, Khaled M. organization: Department of Computer Science and Engineering, Qatar University, Doha, Qatar – sequence: 5 givenname: Raj orcidid: 0000-0002-7023-0368 surname: Jain fullname: Jain, Raj organization: Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, MO, USA
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Snippet	It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine...
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SubjectTerms	Analytics Anomalies Artificial intelligence Cyber attack Cybersecurity Electromagnetic interference Industrial applications Industrial Internet of Things Industrial Internet of Things (IIoT) Internet of Things intrusion detection Intrusion detection systems Literature reviews Machine learning machine learning (ML) network security Protocol (computers) Query languages Structured Query Language-SQL supervisory control and data acquisition (SCADA) Vulnerability vulnerability assessment
Title	Machine Learning-Based Network Vulnerability Analysis of Industrial Internet of Things
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